Update

[beaver.git] / beaver.py

diff --git a/beaver.py b/beaver.py
index 2cc2140..e22fc7b 100755 (executable)
--- a/beaver.py
+++ b/beaver.py
@@ -172,16 +172,16 @@ def compute_perplexity(model, split="train"):
 def one_shot(gpt, task):
     t = gpt.training
     gpt.eval()
+
     model = nn.Sequential(
+        nn.Linear(args.dim_model, args.dim_model),
+        nn.ReLU(),
         nn.Linear(args.dim_model, args.dim_model),
         nn.ReLU(),
         nn.Linear(args.dim_model, 4),
     ).to(device)
 
-    print(f"{args.nb_epochs=}")
-
     for n_epoch in range(args.nb_epochs):
-        print(f"{n_epoch=}")
         learning_rate = learning_rate_schedule[n_epoch]
         optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 
@@ -189,10 +189,14 @@ def one_shot(gpt, task):
         for input, targets in task.policy_batches(split="train"):
             output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
             output = model(output_gpt)
-            loss = (
-                -(output.log_softmax(-1) * targets).sum(-1).mean()
-                + targets.xlogy(targets).sum(-1).mean()
-            )
+            targets = targets * (input.unsqueeze(-1) == maze.v_empty)
+            output = output * (input.unsqueeze(-1) == maze.v_empty)
+            loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
+            # loss = (
+            # -(output.log_softmax(-1) * targets).sum()
+            # / (input == maze.v_empty).sum()
+            # + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
+            # )
             acc_train_loss += loss.item() * input.size(0)
             nb_train_samples += input.size(0)
 
@@ -204,10 +208,14 @@ def one_shot(gpt, task):
         for input, targets in task.policy_batches(split="test"):
             output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
             output = model(output_gpt)
-            loss = (
-                -(output.log_softmax(-1) * targets).sum(-1).mean()
-                + targets.xlogy(targets).sum(-1).mean()
-            )
+            targets = targets * (input.unsqueeze(-1) == maze.v_empty)
+            output = output * (input.unsqueeze(-1) == maze.v_empty)
+            loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
+            # loss = (
+            # -(output.log_softmax(-1) * targets).sum()
+            # / (input == maze.v_empty).sum()
+            # + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
+            # )
             acc_test_loss += loss.item() * input.size(0)
             nb_test_samples += input.size(0)
 
@@ -216,13 +224,15 @@ def one_shot(gpt, task):
         )
 
         # -------------------
-        input, targets = next(task.policy_batches(split="test"))
+        input = task.test_input[:32, : task.height * task.width]
+        targets = task.test_policies[:32]
         output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
         output = model(output_gpt)
-        losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1)
-        losses = losses / losses.max()
-        print(f"{input.size()=} {losses.size()=} {losses.min()=} {losses.max()=}")
-        losses = losses * (input == 0)
+        # losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1)
+        # losses = losses * (input == maze.v_empty)
+        # losses = losses / losses.max()
+        losses = (output.softmax(-1) - targets).abs().max(-1).values
+        losses = (losses >= 0.05).float()
         losses = losses.reshape(-1, args.maze_height, args.maze_width)
         input = input.reshape(-1, args.maze_height, args.maze_width)
         maze.save_image(